@article { , title = {Smart analysis of learners performance using learning analytics for improving academic progression: a case study model.}, abstract = {In the current COVID-19 pandemic era, Learning Management Systems (LMS) are commonly used in e-learning for various learning activities in Higher Education. Learning Analytics (LA) is an emerging area of LMS, which plays a vital role in tracking and storing learners’ activities in the online environment in Higher Education. LA treats the collections of students’ digital footprints and evaluates this data to improve teaching and learning quality. LA measures the analysis and reports learners’ data and their activities to predict decisions on every tier of the education system. This promising area, which both teachers and students can use during this pandemic outbreak, converges LA, Artificial Intelligence, and Human-Centered Design in data visualization techniques, semantic and educational data mining techniques, feature data extraction, etc. Different learning activities of learners for each course are analysed with the help of LA plug-ins. The progression of learners can be monitored and predicted with the help of this intelligent analysis, which aids in improving the academic progress of each learner in a secured manner. The Object-Oriented Programming course and Data Communication Network are used to implement our case studies and to collect the analysis reports. Two plug-ins, local and log store plug-ins, are added to the sample course, and reports are observed. This research collected and monitored the data of the activities each students are involved in. This analysis provides the distribution of access to contents from which the number of active students and students’ activities can be inferred. This analysis provides insight into how many assignment submissions and quiz submissions were on time. The hits distribution is also provided in the analytical chart. Our findings show that teaching methods can be improved based on these inferences as it reflects the students’ learning preferences, especially during this COVID-19 era. Furthermore, each student’s academic progression can be marked and planned in the department.}, doi = {10.3390/su14063378}, eissn = {2071-1050}, issue = {6}, journal = {Sustainability}, note = {INFO COMPLETE (now published 01.02.2023 GB -- Notified by contact of acceptance 31/1/2022 LM) PERMISSION GRANTED (version = VOR; embargo = none; licence = BY; SHERPA = https://v2.sherpa.ac.uk/id/publication/17525 ) DOCUMENT READY (AAM rec'd from contact, to be replaced by VOR when available 11/2/2022 LM) ADDITIONAL INFO - Contact: Ebuka Ibeke Set Statement - (© 2022 by the authors. Accepted in Sustainability for possible open access publication under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/))}, publicationstatus = {Published}, publisher = {MDPI}, url = {https://rgu-repository.worktribe.com/output/1584502}, volume = {14}, keyword = {Learning management system, Learning analytics, Artificial intelligence, Data visualization techniques, LA plug-ins, Teaching, Learning}, year = {2022}, author = {Krishnan, Reshmy and Nair, Sarachandran and Saamuel, Baby Sam and Justin, Sheeba and Iwendi, Celestine and Biamba, Cresantus and Ibeke, Ebuka} }